TY - JOUR
T1 - Data-Driven Edge Offloading for Wireless Control Systems
AU - Ma, Yehan
AU - Chen, Cailian
AU - Zeng, Shen
AU - Guan, Xinping
AU - Lu, Chenyang
N1 - Funding Information:
This work was supported in part by the NSF of China under Grant 62103268, Grant 92167205, Grant 61933009, and Grant 62025305; and in part by the Shanghai Chenguang Program under Grant 21CGA11.
Publisher Copyright:
© 2014 IEEE.
PY - 2023/6/15
Y1 - 2023/6/15
N2 - As industrial plants embrace modern technologies, such as edge computing and wireless networks, industrial control systems have evolved into multitier cyber-physical systems. While traditional local controllers enjoy reliable connectivity to sensors/actuators, they suffer from the limited computation capacity of embedded devices. In contrast, edge servers introduce more computation resources connected to sensors/actuators through wireless networks. Offloading control functions to edge servers presents new opportunities to enhance control performance but also poses critical challenges. As wireless networks have limited bandwidth and varying reliability, it is important to optimize control performance by dynamically offloading a subset of the control functions to edge servers under the bandwidth constraint. Furthermore, the selection of offloaded control functions depends on both the cyber (wireless) and physical states of the wireless control systems. In this article, we tackle the problem of optimizing the control performance of multiple control loops through dynamic edge offloading. We establish a data-driven model to predict the control performance of each feedback control loop based on its cyber-physical states. We then develop a dynamic edge offloading approach to optimize the overall control performance of a system with multiple feedback control loops while guaranteeing their stability under fluctuating cyber-physical conditions. Finally, we demonstrate the efficacy of the data-driven model and offloading approach in case studies comprising simulations of up to 20 industrial robots.
AB - As industrial plants embrace modern technologies, such as edge computing and wireless networks, industrial control systems have evolved into multitier cyber-physical systems. While traditional local controllers enjoy reliable connectivity to sensors/actuators, they suffer from the limited computation capacity of embedded devices. In contrast, edge servers introduce more computation resources connected to sensors/actuators through wireless networks. Offloading control functions to edge servers presents new opportunities to enhance control performance but also poses critical challenges. As wireless networks have limited bandwidth and varying reliability, it is important to optimize control performance by dynamically offloading a subset of the control functions to edge servers under the bandwidth constraint. Furthermore, the selection of offloaded control functions depends on both the cyber (wireless) and physical states of the wireless control systems. In this article, we tackle the problem of optimizing the control performance of multiple control loops through dynamic edge offloading. We establish a data-driven model to predict the control performance of each feedback control loop based on its cyber-physical states. We then develop a dynamic edge offloading approach to optimize the overall control performance of a system with multiple feedback control loops while guaranteeing their stability under fluctuating cyber-physical conditions. Finally, we demonstrate the efficacy of the data-driven model and offloading approach in case studies comprising simulations of up to 20 industrial robots.
KW - Cyber-physical systems
KW - edge offloading
KW - wireless control systems (WCSs)
UR - http://www.scopus.com/inward/record.url?scp=85148470627&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2023.3242770
DO - 10.1109/JIOT.2023.3242770
M3 - Article
AN - SCOPUS:85148470627
SN - 2327-4662
VL - 10
SP - 10802
EP - 10816
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 12
ER -